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Introduction to Robotics in Medicine Part 2: Robotics and Navigation in Radiosurgery

Introduction to Robotics in Medicine Part 2: Robotics and Navigation in Radiosurgery. Achim Schweikard. Stereotactic Radiosurgery. beam. Conventional Procedure . Stereotaxic Frame. Arc motion. Local Anesthesia. Concept. Image-Guidance. Robot System. Robot System.

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Introduction to Robotics in Medicine Part 2: Robotics and Navigation in Radiosurgery

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  1. Introduction to Robotics in MedicinePart 2: Robotics and Navigation in Radiosurgery Achim Schweikard

  2. Stereotactic Radiosurgery beam

  3. Conventional Procedure

  4. Stereotaxic Frame

  5. Arc motion

  6. Local Anesthesia

  7. Concept

  8. Image-Guidance

  9. Robot System

  10. Robot System

  11. System without dynamic tracking

  12. B . . . . . Planning

  13. Sample Case

  14. Phantom (Sample Case)

  15. Donut Shape Dose Distribution GafChromic film TPS

  16. B . . . . . Planning

  17. Planning - Beam Selection - Beam Weighting

  18. Planning - Beam Selection PTV OAR - Beam Weighting

  19. Planning - Beam Selection PTV OAR - Beam Weighting

  20. Planning - Beam Selection PTV OAR - Beam Weighting

  21. Planning - Beam Selection PTV OAR - Beam Weighting

  22. Planning - Beam Selection PTV OAR - Beam Weighting

  23. Planning - Beam Selection PTV OAR More systematic choice? - Beam Weighting

  24. Planning - Beam Selection PTV OAR Nothing is known on exact methods - Beam Weighting

  25. Planning - Beam Selection PTV OAR  Current method is only empirical - Beam Weighting

  26. Planning - Beam Selection - Beam Weighting OAR2 PTV OAR1

  27. Planning - Beam Selection - Beam Weighting OAR2 PTV OAR1

  28. Planning - Beam Selection - Beam Weighting OAR2 PTV OAR1

  29. Planning - Beam Selection - Beam Weighting OAR2 PTV OAR1

  30. Planning - Beam Selection - Beam Weighting OAR2 PTV OAR1

  31. Beam Weighting • Optimization: • Adjust weights s.t. given dose constraints can be met

  32. Beam Weighting • Optimization: • Adjust weights s.t. given dose constraints can be met Zhang P, Dean D, Metzger A, Sibata C. Optimization of Gamma knife treatment planning via guided evolutionary simulated annealing. Med Phys. 2001 Aug;28(8):1746-52.

  33. Beam Weighting • Optimization: • Adjust weights s.t. given dose constraints can be met Zhang P, Dean D, Metzger A, Sibata C. Optimization of Gamma knife treatment planning via guided evolutionary simulated annealing. Med Phys. 2001 Aug;28(8):1746-52. Lessard E, Pouliot J. Inverse planning anatomy-based dose optimization for HDR-brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function. Med Phys. 2001 May;28(5):773-9.

  34. Beam Weighting • Optimization: • Adjust weights s.t. given dose constraints can be met Zhang P, Dean D, Metzger A, Sibata C. Optimization of Gamma knife treatment planning via guided evolutionary simulated annealing. Med Phys. 2001 Aug;28(8):1746-52. Lessard E, Pouliot J. Inverse planning anatomy-based dose optimization for HDR-brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function. Med Phys. 2001 May;28(5):773-9. Rosen II, Lane RG, Morrill SM, Belli JA.Treatment plan optimization using linear programming. Med Phys. 1991 Mar-Apr;18(2):141-52.

  35. c Linear Programming

  36. Linear Programming a11·x1 + ...+ a1n ·xn <= d1 ... ak1 ·x1 + ...+ akn ·xn >= dk ... Max: c1·x1+ ... + cn·xn

  37. Linear Programming • Global Minima /Convergence • fast

  38. Linear Programming for RT

  39. Linear Programming

  40. Linear Programming x1 x2 x3

  41. Linear Programming x1 x2 x3 5

  42. Linear Programming x1 x2 Constraints (Tumor) x1+ x3 >= 5 x3 5

  43. Linear Programming x1 x2 Constraints (Tumor) x1+ x3 >= 5 x3 5 x2+ x3 >= 5 5

  44. Linear Programming x1 x2 Constraints (Tumor) x1+ x3 >= 5 x3 5 x2+ x3 >= 5 . 5 . .

  45. Linear Programming x1 x2 Constraints (Tumor) x1+ x3 >= 5 x3 5 x2+ x3 >= 5 . 5 . . Constraints (Healthy T) x1<= 3 x2<= 3

  46. Linear Programming x1 x2 Constraints (Tumor) x1+ x3 >= 5 x3 5 x2+ x3 >= 5 . 5 . . Constraints (Healthy T) x1<= 3 x2<= 3

  47. Sample Case

  48. Phantom (Sample Case)

  49. Donut Shape Dose Distribution GafChromic film TPS

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